An autonomous vehicle or a sensor assisted vehicle is comprised of some type of sensor system for sensing the environment through which the vehicle navigates. The sensor system preferably has the ability to sense objects in the path of movement of the vehicle so that the vehicle control system can take appropriate action. The sensor system may also be used to sense objects at the sides and to the rear of the vehicle. The vehicle control system may use this sensor information to:
1. alter the speed or steering of the vehicle to avoid collision with objects in the environment,
2. determine position and orientation of features or objects in the environment in order to search for and or follow those features. Examples of such features are walls to be followed down a corridor, door openings for finding passages out of rooms, and corners of walls to find branches in corridors, and smaller objects such as posts that are to be avoided,
3. determine the position and orientation of features of objects in the environment in order to form an associative map that indicates where objects and features are in the environment,
4. determine the position and orientation of objects in the environment that the vehicle's control system will use as navigational landmarks, for registration of the vehicle's drive system while it is following a particular trajectory or to memorize and later locate a particular position in the environment.
Sensor systems that perform the sensing function required for such an application are time-of-flight ranging systems (ultrasound, light, radio waves), as well as laser ranging apparatus and passive scene analysis using computer vision. The ultrasound time-of-flight systems have been preferred because of their low cost.
Most often ultrasonic systems use the time-of-flight technique to measure the distance between the sensor and an object. One or more sensors are excited to send a burst of sound and the same or several other sensors are used to receive the echoes from the environment. Since the velocity of sound in air is known, the time between the emission of the sound burst and the reception of the first echo from an object may be used to determine the distance between the object and the detector.
However, there are a number of problems associated with ultrasonic detectors.
For example, it is difficult to obtain reliable and consistent range signals in most environments. This is due mainly to the specular reflections experienced by ultrasonic signals in most indoor environments. (The wavelength of sound in air is larger than the roughness of most indoor surfaces.)
In order to obtain the angular resolution required to determine the angular position of objects with respect to the vehicle, sensors may be positioned around the vehicle, or concentrated and pointed in the direction of motion of the vehicle. The speed of range data collection is limited by the speed of sound in air (about 340 meters per second, depending mainly on temperature and humidity) and the potential for errors due to the chance of a sound burst from one sensor causing an echo contribution to another sensor. This effect is called crosstalk.
System designers may excite the sensors in a manner that minimizes crosstalk. For example, in a system with a circular array of 24 sensors, orthogonally positioned sensors would be fired simultaneously--four at a time, and range data collected from them. Hence 24 sensors would need 6 excitations to capture the range data from all sensors.
In an attempt to further increase range data acquisition speed, some designers reduce the depth of view for all sensors. This means that the sensor array processor only monitors the echo data lines from the sensors long enough to measure the time of arrival of echoes from objects closer than 1 or 2 meters. In addition, a delay time may be added between firings to allow the previous sound emissions to decay. These techniques require careful adjustment of transducers excitation patterns, delays between firings, and depth of view to avoid crosstalk.
Due to the unreliable nature of the range signals, in order to determine position of objects and features in the environment, a type of temporal and spatial averaging has been used. This involves a vehicle which has a number of sensors positioned around its perimeter. The sensors are excited repetitively while the vehicle moves around the environment. Measurement devices attached to the vehicle's wheels are used to determine the relative motion of the vehicle. Range information regarding potential targets in the environment are recorded along with their positions relative to the vehicle in a two dimensional (2D), map-like representation. As the vehicle moves, the sensors view different volumes of space. This information is used to increase or decrease the probability of a target's appearance within a particular location. Using this system, over the course of time and movement in the environment, a more reliable idea of the position of objects is gained.
This system has a number of problems. It is time consuming, requiring data to be collected over a period of time and required movement in order to determine whether or not the sonar data are reliable.
It requires significant data collecting and storage. Over the course of time or for a large area, a large amount of data may be collected before there are data from sensors in other positions that confirm or deny the position of objects in the environment. To reduce storage requirements and increase processing speed, the granularity of the grid for the two dimensional map may be increased (i.e. cell locations within the map are increased in size). Unfortunately this also reduces the resolution of the system.
Errors due to inaccuracies in the dead reckoning system during movement cause the range data to be distorted.